An Efficient Implementation of Belief Function Propagation
This work addresses computational efficiency issues for researchers and practitioners using belief function propagation in probabilistic reasoning, but it is incremental as it builds on existing local computation techniques.
The paper tackles the problem of inefficient belief function propagation in Markov Trees by presenting an implementation that avoids redundant computations, reducing computational complexity compared to prior methods like Hsia and Shenoy 1989 and Zarley et al. 1988, and includes a combined algorithm for efficient re-propagation when prior beliefs change.
The local computation technique (Shafer et al. 1987, Shafer and Shenoy 1988, Shenoy and Shafer 1986) is used for propagating belief functions in so called a Markov Tree. In this paper, we describe an efficient implementation of belief function propagation on the basis of the local computation technique. The presented method avoids all the redundant computations in the propagation process, and so makes the computational complexity decrease with respect to other existing implementations (Hsia and Shenoy 1989, Zarley et al. 1988). We also give a combined algorithm for both propagation and re-propagation which makes the re-propagation process more efficient when one or more of the prior belief functions is changed.